The first area of precise communication is internal to the field. Authors, reviewers, editors, and journals should provide and demand much more precise reporting in papers, and a suite of associated practices which will facilitate cumulative progress. One step in particular we now feel strongly about is the need to report main effects, by group, at the whole-brain level. It is particularly common to focus on interactions (e.g., task versus control for adolescents versus adults), but that hampers our ability to combine information across studies (e.g., in meta-analyses). For example, in the Silverman et al. (2015) meta-analysis, 62 studies were identified in a literature search addressing adolescent reward sensitivity, but according to the methods only 26 could be included in the meta-analysis, and 21 were excluded specifically because the manuscript did not provide sufficient detail or relied solely upon ROI analysis.
We also feel that more precision in the labeling of regions is critical. For example, there are meaningful differences between components of the basal 3-Deazaneplanocin A – caudate, nucleus accumbens, putamen – and the way in which we often confuse and/or equate these certainly undermines our precision. Other regions that are less well-defined structurally, such as the temporal–parietal junction (TPJ), are sometimes labeled as such with too large a degree of latitude, especially given that different subregions exhibit different anatomical and functional connectivity (for example with respect to the TPJ, see Carter et al., 2012). Still other often-used regional labels – chief among them “prefrontal cortex” – are so imprecise as to be largely uninformative. Even simple lateral and medial distinctions in PFC provide only a fraction more precision. In dual-systems and related models, we propose that a great deal more precision must be achieved especially with respect to subregions of the PFC. An advance in the dual-systems review by Shulman and colleagues (2016) is its assignment of medial PFC and orbitofrontal cortex with the striatum to the “socioemotional system,” distinct from lateral PFC, anterior cingulate cortex, and lateral parietal cortex in the “control system.” However, this quickly becomes complicated since ventromedial PFC has consistently been implicated in regulation networks (Etkin et al., 2011), and dorsomedial PFC is strongly associated with social cognition circuitry (Eickhoff et al., 2014; Bzdok et al., 2013). We believe a key challenge for the development of these models is not only to differentiate social cognitive processes but also to meaningfully integrate them as a key feature of adolescent development, especially in terms of their specific neurobiological substrates. This is an issue that Shulman and colleagues do not address explicitly, although social cognitive processes and networks are clearly a central concern in the social reorientation model by Nelson and colleagues.
Interrogating ROIs as the sole reported analytical approach can be problematic even if defined with a high degree of precision. A targeted ROI analysis may be seen to some extent as a more risky test of one\’s theory, presuming that one has specified directional effects. However, this neglects the exponentially increasing emphasis in the field on networks and circuits (Pfeifer and Allen, 2012; Casey, 2015). Additionally, reporting only ROI analyses limits the contribution that rich whole-brain datasets may provide, particularly when combined with many other studies.
Some conservative data thresholding procedures bias us towards detecting more circumscribed regions with high magnitudes. This is an important practice that supports making more precise regional inferences, unlike the use of lower magnitude thresholds that produce low spatial sensitivity (Woo et al., 2014), but there may be reliable peaks at lower thresholds that will only be identified using whole-brain big data approaches. Furthermore, inclusion of whole-brain data allows for direct comparison of effects observed in predicted and non-predicted regions, once again improving the testing of the specificity of the predicted effects.